Overview

Brought to you by YData

Dataset statistics

Number of variables6
Number of observations28327324
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.5 GiB
Average record size in memory95.6 B

Variable types

Categorical2
Numeric4

Alerts

id_categoria has constant value "1"Constant
liq_um is highly skewed (γ1 = 78.47871768)Skewed
liq_um has 433340 (1.5%) zerosZeros

Reproduction

Analysis started2025-10-18 17:27:41.945858
Analysis finished2025-10-18 17:30:56.528697
Duration3 minutes and 14.58 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

id_categoria
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 GiB
1
28327324 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters28327324
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
128327324
100.0%

Length

2025-10-18T14:30:56.554945image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-18T14:30:56.652552image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
128327324
100.0%

Most occurring characters

ValueCountFrequency (%)
128327324
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)28327324
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
128327324
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)28327324
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
128327324
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)28327324
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
128327324
100.0%

id_cliente
Real number (ℝ)

Distinct97065
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean372401.02
Minimum13
Maximum727517
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size216.1 MiB
2025-10-18T14:30:56.690716image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile60522
Q1190023
median374107
Q3556663
95-th percentile689253
Maximum727517
Range727504
Interquartile range (IQR)366640

Descriptive statistics

Standard deviation203824.56
Coefficient of variation (CV)0.54732546
Kurtosis-1.2000749
Mean372401.02
Median Absolute Deviation (MAD)182834
Skewness-0.072202653
Sum1.0549124 × 1013
Variance4.1544451 × 1010
MonotonicityNot monotonic
2025-10-18T14:30:56.756263image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3103235070
 
< 0.1%
5637424794
 
< 0.1%
3174194738
 
< 0.1%
3869094616
 
< 0.1%
7033854563
 
< 0.1%
3975904541
 
< 0.1%
1196664512
 
< 0.1%
4350324479
 
< 0.1%
2457534426
 
< 0.1%
2296244414
 
< 0.1%
Other values (97055)28281171
99.8%
ValueCountFrequency (%)
1315
 
< 0.1%
33290
 
< 0.1%
511549
< 0.1%
56452
 
< 0.1%
62126
 
< 0.1%
643
 
< 0.1%
86278
 
< 0.1%
122454
 
< 0.1%
14865
 
< 0.1%
1494
 
< 0.1%
ValueCountFrequency (%)
7275171
 
< 0.1%
7275161
 
< 0.1%
7275151
 
< 0.1%
727513179
< 0.1%
727512352
< 0.1%
7275101
 
< 0.1%
727507159
< 0.1%
727505158
< 0.1%
7274949
 
< 0.1%
72749317
 
< 0.1%

id_periodo
Real number (ℝ)

Distinct84
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean202190.02
Minimum201809
Maximum202508
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size216.1 MiB
2025-10-18T14:30:56.818763image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum201809
5-th percentile201902
Q1202010
median202206
Q3202401
95-th percentile202504
Maximum202508
Range699
Interquartile range (IQR)391

Descriptive statistics

Standard deviation199.64598
Coefficient of variation (CV)0.00098741759
Kurtosis-1.0544741
Mean202190.02
Median Absolute Deviation (MAD)195
Skewness-0.11911251
Sum5.7275023 × 1012
Variance39858.519
MonotonicityNot monotonic
2025-10-18T14:30:56.879357image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
202212431300
 
1.5%
202312410059
 
1.4%
202412407005
 
1.4%
202303404418
 
1.4%
202112394008
 
1.4%
202211391564
 
1.4%
202109389822
 
1.4%
202502388200
 
1.4%
202401387052
 
1.4%
202203386607
 
1.4%
Other values (74)24337289
85.9%
ValueCountFrequency (%)
201809260451
0.9%
201810252172
0.9%
201811283500
1.0%
201812314043
1.1%
201901298732
1.1%
201902298273
1.1%
201903299924
1.1%
201904280120
1.0%
201905278713
1.0%
201906252741
0.9%
ValueCountFrequency (%)
202508325489
1.1%
202507333226
1.2%
202506308601
1.1%
202505337662
1.2%
202504341065
1.2%
202503385217
1.4%
202502388200
1.4%
202501385482
1.4%
202412407005
1.4%
202411364850
1.3%

tipo_mix
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 GiB
PREMIUM
15605459 
MASIVO
12721865 

Length

Max length7
Median length7
Mean length6.5508977
Min length6

Characters and Unicode

Total characters185569403
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPREMIUM
2nd rowPREMIUM
3rd rowPREMIUM
4th rowPREMIUM
5th rowPREMIUM

Common Values

ValueCountFrequency (%)
PREMIUM15605459
55.1%
MASIVO12721865
44.9%

Length

2025-10-18T14:30:56.936039image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-18T14:30:56.967584image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
premium15605459
55.1%
masivo12721865
44.9%

Most occurring characters

ValueCountFrequency (%)
M43932783
23.7%
I28327324
15.3%
R15605459
 
8.4%
P15605459
 
8.4%
E15605459
 
8.4%
U15605459
 
8.4%
A12721865
 
6.9%
S12721865
 
6.9%
V12721865
 
6.9%
O12721865
 
6.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)185569403
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M43932783
23.7%
I28327324
15.3%
R15605459
 
8.4%
P15605459
 
8.4%
E15605459
 
8.4%
U15605459
 
8.4%
A12721865
 
6.9%
S12721865
 
6.9%
V12721865
 
6.9%
O12721865
 
6.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)185569403
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M43932783
23.7%
I28327324
15.3%
R15605459
 
8.4%
P15605459
 
8.4%
E15605459
 
8.4%
U15605459
 
8.4%
A12721865
 
6.9%
S12721865
 
6.9%
V12721865
 
6.9%
O12721865
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)185569403
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M43932783
23.7%
I28327324
15.3%
R15605459
 
8.4%
P15605459
 
8.4%
E15605459
 
8.4%
U15605459
 
8.4%
A12721865
 
6.9%
S12721865
 
6.9%
V12721865
 
6.9%
O12721865
 
6.9%

id_sku_venta
Real number (ℝ)

Distinct520
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean428579.43
Minimum515
Maximum604926
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size216.1 MiB
2025-10-18T14:30:57.010407image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum515
5-th percentile7493
Q1450325
median450604
Q3450749
95-th percentile451269
Maximum604926
Range604411
Interquartile range (IQR)424

Descriptive statistics

Standard deviation104985.77
Coefficient of variation (CV)0.24496222
Kurtosis12.112201
Mean428579.43
Median Absolute Deviation (MAD)201
Skewness-3.5935882
Sum1.2140508 × 1013
Variance1.1022012 × 1010
MonotonicityNot monotonic
2025-10-18T14:30:57.072068image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4506071159881
 
4.1%
450604981474
 
3.5%
450592918378
 
3.2%
450684914142
 
3.2%
450237907067
 
3.2%
450133852698
 
3.0%
450403749381
 
2.6%
592741858
 
2.6%
450746728508
 
2.6%
450417721352
 
2.5%
Other values (510)19652585
69.4%
ValueCountFrequency (%)
51565937
 
0.2%
566369
 
< 0.1%
592741858
2.6%
595130305
 
0.5%
622116009
 
0.4%
76323991
 
0.1%
76580408
 
0.3%
748114234
 
0.1%
748212103
 
< 0.1%
748359447
 
0.2%
ValueCountFrequency (%)
6049263169
 
< 0.1%
6045832105
 
< 0.1%
60458234283
 
0.1%
604581390476
1.4%
60458042379
 
0.1%
60453345
 
< 0.1%
604514494
 
< 0.1%
60450932340
 
0.1%
6045085513
 
< 0.1%
4515007287
 
< 0.1%

liq_um
Real number (ℝ)

Skewed  Zeros 

Distinct16318
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.93081289
Minimum-89.46
Maximum3096.6533
Zeros433340
Zeros (%)1.5%
Negative4395
Negative (%)< 0.1%
Memory size216.1 MiB
2025-10-18T14:30:57.130691image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-89.46
5-th percentile0.0462
Q10.084
median0.21
Q30.588
95-th percentile2.94
Maximum3096.6533
Range3186.1133
Interquartile range (IQR)0.504

Descriptive statistics

Standard deviation7.3722788
Coefficient of variation (CV)7.9202586
Kurtosis12213.228
Mean0.93081289
Median Absolute Deviation (MAD)0.1512
Skewness78.478718
Sum26367438
Variance54.350495
MonotonicityNot monotonic
2025-10-18T14:30:57.196365image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.078961776867
 
6.3%
0.055441702889
 
6.0%
0.0841338104
 
4.7%
0.157921214046
 
4.3%
0.0588912625
 
3.2%
0.168902608
 
3.2%
0.11088780586
 
2.8%
0.05964754101
 
2.7%
0.23688752916
 
2.7%
0.042651926
 
2.3%
Other values (16308)17540656
61.9%
ValueCountFrequency (%)
-89.461
< 0.1%
-63.1681
< 0.1%
-56.85121
< 0.1%
-52.982161
< 0.1%
-47.77081
< 0.1%
-38.348521
< 0.1%
-35.7841
< 0.1%
-33.39841
< 0.1%
-33.2641
< 0.1%
-31.5841
< 0.1%
ValueCountFrequency (%)
3096.653281
< 0.1%
2949.94561
< 0.1%
2527.667521
< 0.1%
2349.84961
< 0.1%
2336.979121
< 0.1%
2128.76161
< 0.1%
2021.3761
< 0.1%
1958.2081
< 0.1%
1953.47041
< 0.1%
1895.041
< 0.1%

Interactions

2025-10-18T14:30:29.377125image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:29:54.783237image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:30:06.059747image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:30:17.648393image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:30:32.462744image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:29:57.672385image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:30:08.746663image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:30:20.654532image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:30:35.309669image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:30:00.465159image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:30:11.515824image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:30:23.462125image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:30:38.115659image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:30:03.287415image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:30:14.562395image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:30:26.437908image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-10-18T14:30:57.244148image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
id_clienteid_periodoid_sku_ventaliq_umtipo_mix
id_cliente1.000-0.007-0.017-0.0030.054
id_periodo-0.0071.0000.293-0.0800.171
id_sku_venta-0.0170.2931.000-0.1150.199
liq_um-0.003-0.080-0.1151.0000.010
tipo_mix0.0540.1710.1990.0101.000

Missing values

2025-10-18T14:30:38.890904image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-10-18T14:30:43.641927image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

id_categoriaid_clienteid_periodotipo_mixid_sku_ventaliq_um
01693134202110PREMIUM4504034.32432
11693134202110PREMIUM4504171.17600
21693134202110PREMIUM4506847.26432
31693134202110PREMIUM4507040.22176
41693134202110PREMIUM4507463.47424
51693134202110PREMIUM4507490.67200
61693134202110PREMIUM4508500.55272
71693134202110PREMIUM4508511.57920
81693134202110PREMIUM4508590.33264
91693134202111MASIVO4501330.23688
id_categoriaid_clienteid_periodotipo_mixid_sku_ventaliq_um
283273141658679202508PREMIUM74930.05544
283273151658679202508PREMIUM4502950.05544
283273161658679202508PREMIUM4503020.05544
283273171658679202508PREMIUM4503360.05964
283273181658679202508PREMIUM4504030.05544
283273191658679202508PREMIUM4507040.05544
283273201658679202508PREMIUM6045810.05544
283273211658553202508PREMIUM4503020.05544
283273221658553202508PREMIUM4505510.21000
283273231658553202508PREMIUM4513710.21000